code: def main(args):
      if args.seed is not None:
          paddle.seed(args.seed)
          np.random.seed(args.seed)
          random.seed(args.seed)
  
      env_info = get_sys_env()
      info = ["{}: {}".format(k, v) for k, v in env_info.items()]
      info = "\n".join(
          ["", format("Environment Information", "-^48s")]
          + info
          + ["-" * 48]
      )
      logger.info(info)
  
      place = (
          "gpu"
          if env_info["Paddle compiled with cuda"]
          and env_info["GPUs used"]
          else "cpu"
      )
  
      paddle.set_device(place)
      if not args.cfg:
          raise RuntimeError("No configuration file specified.")
  
      cfg = Config(
          args.cfg,
          learning_rate=args.learning_rate,
          iters=args.iters,
          batch_size=args.batch_size,
      )
  
      # Only support for the DeepLabv3+ model
      if args.data_format == "NHWC":
          if cfg.dic["model"]["type"] != "DeepLabV3P":
              raise ValueError(
                  'The "NHWC" data format only support the DeepLabV3P model!'
              )
          cfg.dic["model"]["data_format"] = args.data_format
          cfg.dic["model"]["backbone"]["data_format"] = args.data_format
          loss_len = len(cfg.dic["loss"]["types"])
          for i in range(loss_len):
              cfg.dic["loss"]["types"][i][
                  "data_format"
              ] = args.data_format
  
      train_dataset = cfg.train_dataset
      if train_dataset is None:
          raise RuntimeError(
              "The training dataset is not specified in the configuration file."
          )
      elif len(train_dataset) == 0:
          raise ValueError(
              "The length of train_dataset is 0. Please check if your dataset is valid"
          )
      val_dataset = cfg.val_dataset if args.do_eval else None
      losses = cfg.loss
  
      msg = "\n---------------Config Information---------------\n"
      msg += str(cfg)
      msg += "------------------------------------------------"
      logger.info(msg)
  
      if args.nnunet:
          nnunet.core.train(
              cfg.model,
              train_dataset,
              val_dataset=val_dataset,
              optimizer=cfg.optimizer,
              save_dir=args.save_dir,
              iters=cfg.iters,
              batch_size=cfg.batch_size,
              resume_model=args.resume_model,
              save_interval=args.save_interval,
              log_iters=args.log_iters,
              num_workers=args.num_workers,
              use_vdl=args.use_vdl,
              losses=losses,
              keep_checkpoint_max=args.keep_checkpoint_max,
              precision=args.precision,
              profiler_options=args.profiler_options,
              to_static_training=cfg.to_static_training,
          )
      else:
          train(
              cfg.model,
              train_dataset,
              val_dataset=val_dataset,
              optimizer=cfg.optimizer,
              save_dir=args.save_dir,
              iters=cfg.iters,
              batch_size=cfg.batch_size,
              resume_model=args.resume_model,
              save_interval=args.save_interval,
              log_iters=args.log_iters,
              num_workers=args.num_workers,
              use_vdl=args.use_vdl,
              losses=losses,
              keep_checkpoint_max=args.keep_checkpoint_max,
              profiler_options=args.profiler_options,
              to_static_training=cfg.to_static_training,
              sw_num=args.sw_num,
              is_save_data=args.is_save_data,
              has_dataset_json=args.has_dataset_json,
          )
  
file_path: PaddleSeg/contrib/MedicalSeg/train.py
imports: import random
  import argparse
  import paddle
  import numpy
  from medicalseg.cvlibs import manager, Config
  from medicalseg.utils import get_sys_env, logger
  from medicalseg.core import train
  import nnunet
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types